The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method

DOI: 10.4236/jilsa.2013.54026   PDF   HTML     3,958 Downloads   5,602 Views   Citations

Abstract

This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.

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Q. Li, Y. Qin, Z. Wang, Z. Zhao, M. Zhan, Y. Liu and Z. Li, "The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 4, 2013, pp. 227-231. doi: 10.4236/jilsa.2013.54026.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] G. W. Chang and W. W. Shi, “Study on Short-time Passenger Flow Forecast and the Optimal Capacity Allocation Problem in Urban Mass Trasit,” Beijing Jiaotong University, Beijing, 2011.
[2] J. Mao, “Urban Rail Transit Passenger Flow Short-Term Prediction Method and Empirical Research,” Beijing Jiaotong University, Beijing, 2012.
[3] R. Yang, “Study on Passenger Flow Forecast and Operation Scheduling Method of Urban Rail Transit,” Beijing Jiaotong University, Beijing, 2010.
[4] L. N. Wang, “Traffic Prediction and Scheduling of Urban Passenger Rail Network Based on Historical Data,” Beijing Jiaotong University, Beijing, 2011.
[5] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Representations by Back-Propagation Errors,” Nature, Vol. 323, 1986, pp. 533-536.
http://dx.doi.org/10.1038/323533a0
[6] P. Jiang, Q. Shi, W. W. Chen, et al., “Prediction of Passenger Volume Based on Elman Type Recurrent Neural Network,” Journal of Hefei University of Technology (Science), Vol. 31, No. 3, 2008, pp. 340-342, 369.

  
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